{"id":51231,"date":"2022-05-09T21:50:56","date_gmt":"2022-05-09T21:50:56","guid":{"rendered":"https:\/\/www.thepicpedia.com\/faq\/how-do-i-set-gpu-for-jupyter-notebook\/"},"modified":"2022-05-09T21:50:56","modified_gmt":"2022-05-09T21:50:56","slug":"how-do-i-set-gpu-for-jupyter-notebook","status":"publish","type":"post","link":"https:\/\/www.thepicpedia.com\/faq\/how-do-i-set-gpu-for-jupyter-notebook\/","title":{"rendered":"How do i set gpu for jupyter notebook?"},"content":{"rendered":"
In your PC’s Start menu, type “Device Manager,” and press Enter to launch the Control Panel’s Device Manager. Click the drop-down arrow next to Display adapters, and it should list your GPU right there.<\/p>\n
NVIDIA’s CUDA Python provides a driver and runtime API for existing toolkits and libraries to simplify GPU-based accelerated processing. Python is one of the most popular programming languages for science, engineering, data analytics, and deep learning applications.<\/p>\n<\/p>\n
CUDA\u00ae is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).<\/p>\n<\/p>\n
GPGPU Programming is general purpose computing with the use of a Graphic Processing Unit (GPU). This is done by using a GPU together with a Central Processing Unit (CPU) to accelerate the computations in applications that are traditionally handled by just the CPU only.<\/p>\n<\/p>\n
If a TensorFlow operation has both CPU and GPU implementations, TensorFlow will automatically place the operation to run on a GPU device first. If you have more than one GPU, the GPU with the lowest ID will be selected by default. However, TensorFlow does not place operations into multiple GPUs automatically.<\/p>\n<\/p>\n